[1]蒲国林,刘笃晋.基于改进神经网络的环境空气质量预测[J].计算机技术与发展,2018,28(09):181-184.[doi:10.3969/ j. issn.1673-629X.2018.09.037]
　PU Guo-lin,LIU Du-jin.Ambient Air Quality Prediction Based on Improved Neural Network[J].,2018,28(09):181-184.[doi:10.3969/ j. issn.1673-629X.2018.09.037]
点击复制

基于改进神经网络的环境空气质量预测()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年09期
页码:
181-184
栏目:
应用开发研究
出版日期:
2018-09-10

文章信息/Info

Title:
Ambient Air Quality Prediction Based on Improved Neural Network
文章编号:
1673-629X(2018)09-0181-04
作者:
蒲国林; 刘笃晋
四川文理学院 智能制造学院,四川 达州 635000
Author(s):
PU Guo-lin; LIU Du-jin
School of Intelligent Manufacturing,Sichuan University of Arts and Science,Dazhou 635000,China
关键词:
人工蜂群算法; 迭代递减; 反向传播神经网络; 环境空气质量预测; 误差函数; 适应度函数
Keywords:
artificial bee colony algorithm; iterative descending; back propagation neural network; ambient air quality prediction; error function; fitness function
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.09.037
文献标志码:
A
摘要:
为提高环境空气质量预测的精度,提出一种由改进人工蜂群算法和反向传播神经网络相结合的环境空气质量预测方法(KABC-BP)。 对人工蜂群算法中雇佣蜂、跟随蜂的搜索空间提出一种随迭代次数递减的搜索公式,以随机初始化此改进人工蜂群算法的不同初始解作为不同组反向传播神经网络权值,以蜂群算法迭代代替人工神经网络的梯度下降修正迭代,以蜂群个体的对应权值下训练误差倒数作为适应度函数,该改进人工蜂群算法所求全局最优解就是所求反向神经网络最优权值。 通过基于改进蜂群算法的反向传播神经网络算法、传统蜂群算法的反向传播神经网络算法(ABC-BP)及反向传播神经网络算法(BPNN)的环境空气质量预测的仿真实验表明,该算法的环境空气质量预测精度是最高的。
Abstract:
In order to improve the accuracy of ambient air quality prediction,we propose a method of ambient air quality prediction based on improved artificial bee colony algorithm and back propagation neural network (KABC-BP). In the artificial bee colony algorithm,we give a search formula with decreasing number of iterations for the search space of employed bees and onlookers. The different initial solutions of the improved artificial bee colony algorithm are randomly initialized as the weights of different groups of back propagation neural networks. The gradient iteration of artificial neural network is replaced by iterative algorithm of artificial bee colony algorithm. The reciprocal of training errors is used as fitness function under the corresponding weight of colony individuals. The global optimal solution of the improved artificial bee colony algorithm is the optimal weight of the back propagation neural network. The simulation of ambient air quality prediction on back propagation neural network algorithm based on improved bee colony algorithm,back propagation neural network algorithm based on the traditional bee colony algorithm (ABC-BP) and back propagation neural network algorithm (BPNN) shows that the method proposed is the highest in the prediction of ambient air quality.

相似文献/References:

[1]何志明 马苗.基于灰色关联分析和人工蜂群算法的图像匹配方法[J].计算机技术与发展,2010,(10):78.
　HE Zhi-ming,MA Miao.Fast Image Matching Approach Based on Grey Relational Analysis and Artificial Bee Colony Algorithm[J].,2010,(09):78.
[2]李林菲 马苗.基于ABC算法的逻辑推理题快速求解方法[J].计算机技术与发展,2011,(06):125.
　LI Lin-fei,MA Miao.Artificial Bee Colony Algorithm Based Solution Method for Logic Reasoning[J].,2011,(09):125.
[3]于君 刘弘.基于ABC算法的群体动画研究与应用[J].计算机技术与发展,2011,(10):222.
　YU Jun,LIU Hong.Research and Implementation of Group Animation Based on Artificial Bee Colony Algorithm[J].,2011,(09):222.
[4]杨小东,刘波.人工蜂群算法加速收敛技术研究[J].计算机技术与发展,2014,24(04):25.
　YANG Xiao-dong,LIU Bo.Research on Accelerating Convergence Technique of Artificial Bee Colony Algorithm[J].,2014,24(09):25.
[5]贾冀婷. 基于K均值PSOABC的测试用例自动生成方法[J].计算机技术与发展,2015,25(06):12.
　JIA Ji-ting. Automatic Testcase Generation Method Based on PSOABC and K-means Clustering Algorithm[J].,2015,25(09):12.
[6]刘立群[],韩俊英[],代永强[],等. 果蝇优化算法优化性能对比研究[J].计算机技术与发展,2015,25(08):94.
　LIU Li-qun[],HAN Jun-ying[],DAI Yong-qiang[],et al. Comparative Study on Optimization Performance of Fruit Fly Optimization Algorithm [J].,2015,25(09):94.
[7]王野,周井泉,常瑞云. 基于知识的人工蜂群服务组合优化算法[J].计算机技术与发展,2016,26(05):46.
　WANG Ye,ZHOU Jing-quan,CHANG Rui-yun. Artificial Bee Colony Algorithm for Service Composition Based on Knowledge[J].,2016,26(09):46.
[8]娄艳秋[],庄毅[],顾晶晶[],等. 协同干扰环境下基于IMOABC的任务调度方法[J].计算机技术与发展,2017,27(11):46.
　LOU Yan-qiu[],ZHUANG Yi[],GU Jing-jing[],et al. A Task Scheduling Method Based on IMOABC in Collaboration Interference Environment[J].,2017,27(09):46.
[9]李鑫鑫,刘群锋.基于改进人工蜂群算法的多阈值图像分割[J].计算机技术与发展,2023,33(05):75.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 012]
　LI Xin-xin,LIU Qun-feng.Multi-threshold Image Segmentation Based on Improved Artificial Bee Colony Algorithm[J].,2023,33(09):75.[doi:10. 3969 / j. issn. 1673-629X. 2023. 05. 012]
[10]马 卫*,李微微.改进人工蜂群算法的点云数据配准优化研究[J].计算机技术与发展,2023,33(06):79.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 013]
　MA Wei*,LI Wei-wei.Research on Point Clouds Data Registration Optimization Based on Improved Artificial Bee Colony Algorithm[J].,2023,33(09):79.[doi:10. 3969 / j. issn. 1673-629X. 2023. 06. 013]

更新日期/Last Update: 2018-09-10